Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

Yin, Gaohong; Mariethoz, Grégoire; McCabe, Matthew F.

doi:10.3390/rs9010012

Cited by 78 publications

(44 citation statements)

References 31 publications

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“…The unknown error in the CRPS estimation may lead to the wrong choice of the best forecast. Although meteorological vocabulary is used, this situation can occur in other fields of geosciences too, for instance when conditional simulations are used to sample from a probability distribution and choose between competing techniques or settings (Emery and Lantuéjoul 2006;Pirot et al 2014;Yin et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Zamo

Naveau

2017

Math Geosci

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The continuous ranked probability score (CRPS) is a much used measure of performance for probabilistic forecasts of a scalar observation. It is a quadratic measure of the difference between the forecast cumulative distribution function (CDF) and the empirical CDF of the observation. Analytic formulations of the CRPS can be derived for most classical parametric distributions, and be used to assess the efficiency of different CRPS estimators. When the true forecast CDF is not fully known, but represented as an ensemble of values, the CRPS is estimated with some error. Thus, using the CRPS to compare parametric probabilistic forecasts with ensemble forecasts may be misleading due to the unknown error of the estimated CRPS for the ensemble. With simulated data, the impact of the type of the verified ensemble (a random sample or a set of quantiles) on the CRPS estimation is studied. Based on these simulations, recommendations are issued to choose the most accurate CRPS estimator according to the type of ensemble. The interest of these recommendations is illustrated with real ensemble weather forecasts. Also, relationships between several estimators of the CRPS are demonstrated and used to explain the differences of accuracy between the estimators.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Zamo

Naveau

2017

Math Geosci

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“…The MSA (16) measures the spectral similarity between the predicted and the observed spectra [28]. By considering these as vectors in a space b-dimensional, the angle between them is calculated in a range from 0 • (high similarity) to 90 • (high dissimilarity).…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

“…From the aforementioned approaches, the geostatistical methods have demonstrated strong cloud removal and gap-filling performance [19], although with some limitations when applied to heterogeneous and dynamic landscapes or with large data gaps [28]. They can also be computationally expensive, making their routine application challenging [22].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery

2019

Self Cite

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One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1° and 11°, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.

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“…For example, the Scan Line Corrector (SLC; an electromechanical device that compensates for the forward motion of Landsat 7) failed on 31 May 2003, resulting in up to 22% of the pixels missing in the collected image scenes. 24 The recently launched Landsat 8 Operational Land Imager (OLI) with improved signalto-noise ratio and 12-bit quantisation exhibited the potential to map chl-a by using empirical band ratio regression models. 25 This potential is because of its stripe-free images, as opposed to the striped images acquired by Landsat 7 ETM+ over the local study area.…”

Section: Introductionmentioning

confidence: 99%

Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data

Malahlela¹,

Oliphant²,

Tsoeleng³

et al. 2018

S. Afr. J. Sci.

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Mapping chlorophyll-a (chl-a) is crucial for water quality management in turbid and productive case II water bodies, which are largely influenced by suspended sediment and phytoplankton. Recent developments in remote sensing technology offer new avenues for water quality assessment and chl-a detection for inland water bodies. In this study, the red to near-infrared (NIR-red) bands were tested for the Vaal Dam in South Africa to classify chl-a concentrations using Landsat 8 Operational Land Imager (OLI) data for 2014–2016 by means of stepwise logistic regression (SLR). The moderate-resolution imaging spectroradiometer (MODIS) data were also used for validating chl-a concentration classes. The chl-a concentrations were classified into low and high concentrations. The SLR applied on 2014 images yielded an overall accuracy of 80% and kappa coefficient (κ) of 0.74 on April 2014 data, while an overall accuracy of 65% and κ=0.30 were obtained for the May 2015 Landsat data. There was a significant (p less than 0.05) negative correlation between chl-a classes and red band in all analyses, while the NIR band showed a positive correlation (0.0001; p less than 0.89) for April 2014 data set. The 2015 image classification yielded an overall accuracy of 83% and κ=0.43. The difference vegetation index showed a significant (p less than 0.003) positive correlation with chl-a concentrations for May 2015 and July 2016, with chl-a ranges of between 2.5 μg/L and 1219 μg/L. These correlations show that a class increase in chl-a (from low to high) is in response to an increase in greenness within the Vaal Dam. We have demonstrated the applicability of Landsat 8 OLI data for inland water quality assessment.

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Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method

Cited by 78 publications

References 31 publications

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts

Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery

Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data

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